CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: dictionary iearning</title> <meta name="description" content="Search results for: dictionary iearning"> <meta name="keywords" content="dictionary iearning"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="dictionary iearning" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="dictionary iearning"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 91</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: dictionary iearning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">91</span> An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanwen%20Li">Yanwen Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuguo%20Xie"> Shuguo Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gradient%20image" title="gradient image">gradient image</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20and%20extract" title=" segmentation and extract"> segmentation and extract</a>, <a href="https://publications.waset.org/abstracts/search?q=mean-shift%20algorithm" title=" mean-shift algorithm"> mean-shift algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20iearning" title=" dictionary iearning"> dictionary iearning</a> </p> <a href="https://publications.waset.org/abstracts/74979/an-image-segmentation-algorithm-for-gradient-target-based-on-mean-shift-and-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74979.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">266</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">90</span> The Efficiency of the Use of Medical Bilingual Dictionary in English Language Teaching in Vocational College</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zorana%20Jurinjak">Zorana Jurinjak</a>, <a href="https://publications.waset.org/abstracts/search?q=Christos%20Alexopoulos"> Christos Alexopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to examine the effectiveness of using a medical bilingual dictionary in teaching English in a vocational college. More precisely, to what extent the use of bilingual medical dictionary in relation to the use of Standard English bilingual dictionaries influences the results on tests, and thus the acquisition of better competence of students mastering the subject terminology. Secondary interest in this paper would be to raise awareness among students and teachers about the advantages of dictionary use. The experiment was conducted at College of Applied Health Sciences in Ćuprija on a sample of 90 students. The respondents translated three medical texts with 42 target terms. Statistical analyses of the data obtained show that the differences in average time and correct answers favor the students who used medical dictionary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bilingual%20medical%20dictionary" title="bilingual medical dictionary">bilingual medical dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=standard%20english%20bilingual%20dictionary" title=" standard english bilingual dictionary"> standard english bilingual dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20terminology" title=" medical terminology"> medical terminology</a>, <a href="https://publications.waset.org/abstracts/search?q=EOS" title=" EOS"> EOS</a>, <a href="https://publications.waset.org/abstracts/search?q=ESP" title=" ESP"> ESP</a> </p> <a href="https://publications.waset.org/abstracts/148251/the-efficiency-of-the-use-of-medical-bilingual-dictionary-in-english-language-teaching-in-vocational-college" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148251.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">110</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">89</span> The Grammatical Dictionary Compiler: A System for Kartvelian Languages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liana%20Lortkipanidze">Liana Lortkipanidze</a>, <a href="https://publications.waset.org/abstracts/search?q=Nino%20Amirezashvili"> Nino Amirezashvili</a>, <a href="https://publications.waset.org/abstracts/search?q=Nino%20Javashvili"> Nino Javashvili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of the grammatical dictionary is to provide information on the morphological and syntactic characteristics of the basic word in the dictionary entry. The electronic grammatical dictionaries are used as a tool of automated morphological analysis for texts processing. The Georgian Grammatical Dictionary should contain grammatical information for each word: part of speech, type of declension/conjugation, grammatical forms of the word (paradigm), alternative variants of basic word/lemma. In this paper, we present the system for compiling the Georgian Grammatical Dictionary automatically. We propose dictionary-based methods for extending grammatical lexicons. The input lexicon contains only a few number of words with identical grammatical features. The extension is based on similarity measures between features of words; more precisely, we add words to the extended lexicons, which are similar to those, which are already in the grammatical dictionary. Our dictionaries are corpora-based, and for the compiling, we introduce the method for lemmatization of unknown words, i.e., words of which neither full form nor lemma is in the grammatical dictionary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acquisition%20of%20lexicon" title="acquisition of lexicon">acquisition of lexicon</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgian%20grammatical%20dictionary" title=" Georgian grammatical dictionary"> Georgian grammatical dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=lemmatization%20rules" title=" lemmatization rules"> lemmatization rules</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20processor" title=" morphological processor"> morphological processor</a> </p> <a href="https://publications.waset.org/abstracts/116347/the-grammatical-dictionary-compiler-a-system-for-kartvelian-languages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116347.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">88</span> The Analysis of Indian Culture through the Lexicographical Discourse of Hindi-French Dictionary</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tanzil%20Ansari">Tanzil Ansari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A dictionary is often considered as a list of words, arranged in alphabetical orders, providing information on a language or languages and it informs us about the spelling, the pronunciation, the origin, the gender and the grammatical functions of new and unknown words. In other words, it is first and foremost a linguistic tool. But, the research across the world in the field of linguistic and lexicography proved that a dictionary is not only a linguistic tool but also a cultural product through which a lexicographer transmits the culture of a country or a linguistic community from his or her ideology. It means, a dictionary does not present only language and its metalinguistic functions but also its culture. Every language consists of some words and expressions which depict the culture of its language. In this way, it is impossible to disassociate language from its culture. There is always an ideology that plays an important role in the depiction of any culture. Using the orientalism theory of Edward Said to represent the east, the objective of the present research is to study the representation of Indian culture through the lexicographical discourse of Hindi-French Dictionary of Federica Boschetti, a French lexicographer. The results show that the Indian culture is stereotypical and monolithic. It also shows India as male oriented country where women are exploited by male-dominated society. The study is focused on Hindi-French dictionary, but its line of argument can be compared to dictionaries produced in other languages. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=culture" title="culture">culture</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary" title=" dictionary"> dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=lexicographical%20discourse" title=" lexicographical discourse"> lexicographical discourse</a>, <a href="https://publications.waset.org/abstracts/search?q=stereotype%20image" title=" stereotype image "> stereotype image </a> </p> <a href="https://publications.waset.org/abstracts/74279/the-analysis-of-indian-culture-through-the-lexicographical-discourse-of-hindi-french-dictionary" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74279.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">300</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">87</span> An Online Corpus-Based Bilingual Collocations Dictionary for Second/Foreign Language Learners</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adriane%20Orenha-Ottaiano">Adriane Orenha-Ottaiano</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collocations are conventionalized, recurrent and arbitrary lexical combinations. Due to the fact that they are highly specific for a particular language and may be contextually restricted, collocations pose a problem to EFL/ESL learners with regard to production or encoding. Taking that into account, the compilation of monolingual and bilingual collocations dictionaries for the referred audience is highly crucial and significant. Thus, the aim of this paper is to discuss the importance of the compilation of an Online Corpus-based Bilingual Collocations Dictionary, in the English-Portuguese and Portuguese-English directions. On a first phase, with the use of WordSmith Tools, the collocations were extracted from a Translation Learner Corpus (TLC), a parallel corpus made up of university students’ translations in the Portuguese-English direction, with approximately 100,000 words. In a second stage, based on the keywords analyzed from the TLC, more collocational patterns were extracted using the Sketch Engine. In order to include more collocations as well as to ensure dictionary users will have access to more frequent and recurrent collocations, we also use the frequency list from The Corpus of Contemporary American English, with the purpose of extracting more patterns. The dictionary focuses on all types of collocations (verbal, noun, adjectival and adverbial collocations), in order to help the referred audience use them more accurately and productively – so far the dictionary has more than 330 entries, and more than 3,500 collocations extracted. The idea of having the proposed dictionary in online format may allow to incorporate more qualitatively and quantitatively collocational information. Besides, more examples may be included, different from conventional printed collocations dictionaries. Being the first bilingual collocations dictionary in the aforementioned directions, it is hoped to achieve the challenge of meeting learners’ collocational needs as the collocations have been selected according to learners’ difficulties regarding the use of collocations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Corpus-Based%20Collocations%20Dictionary" title="Corpus-Based Collocations Dictionary">Corpus-Based Collocations Dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=Collocations" title=" Collocations "> Collocations </a>, <a href="https://publications.waset.org/abstracts/search?q=Bilingual%20Collocations%20Dictionary" title=" Bilingual Collocations Dictionary"> Bilingual Collocations Dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=Collocational%20Patterns" title=" Collocational Patterns"> Collocational Patterns</a> </p> <a href="https://publications.waset.org/abstracts/65812/an-online-corpus-based-bilingual-collocations-dictionary-for-secondforeign-language-learners" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65812.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">86</span> Music Note Detection and Dictionary Generation from Music Sheet Using Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ammar">Muhammad Ammar</a>, <a href="https://publications.waset.org/abstracts/search?q=Talha%20Ali"> Talha Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Basit"> Abdul Basit</a>, <a href="https://publications.waset.org/abstracts/search?q=Bakhtawar%20Rajput"> Bakhtawar Rajput</a>, <a href="https://publications.waset.org/abstracts/search?q=Zobia%20Sohail"> Zobia Sohail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=music%20note" title="music note">music note</a>, <a href="https://publications.waset.org/abstracts/search?q=sheet%20music" title=" sheet music"> sheet music</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20music%20recognition" title=" optical music recognition"> optical music recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=blob%20detection" title=" blob detection"> blob detection</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20generation" title=" dictionary generation"> dictionary generation</a> </p> <a href="https://publications.waset.org/abstracts/133670/music-note-detection-and-dictionary-generation-from-music-sheet-using-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133670.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">181</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">85</span> The First Japanese-Japanese Dictionary for Non-Japanese Using the Defining Vocabulary</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Minoru%20Moriguchi">Minoru Moriguchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research introduces the concept of a monolingual Japanese dictionary for non-native speakers of Japanese, whose temporal title is Dictionary of Contemporary Japanese for Advanced Learners (DCJAL). As the language market is very small compared with English, a monolingual Japanese dictionary for non-native speakers, containing sufficient entries, has not been published yet. In such a dictionary environment, Japanese-language learners are using bilingual dictionaries or monolingual Japanese dictionaries for Japanese people. This research started in 2017, as a project team which consists of four Japanese and two non-native speakers, all of whom are linguists of the Japanese language. The team has been trying to propose the concept of a monolingual dictionary for non-native speakers of Japanese and to provide the entry list, the definition samples, the list of defining vocabulary, and the writing manual. As the result of seven-year research, DCJAL has come to have 28,060 head words, 539 entry examples, 4,598-word defining vocabulary, and the writing manual. First, the number of the entry was determined as about 30,000, based on an experimental method using existing six dictionaries. To make the entry list satisfying this number, words suitable for DCJAL were extracted from the Tsukuba corpus of the Japanese language, and later the entry list was adjusted according to the experience as Japanese instructor. Among the head words of the entry list, 539 words were selected and added with lexicographical information such as proficiency level, pronunciation, writing system (hiragana, katakana, kanji, or alphabet), definition, example sentences, idiomatic expression, synonyms, antonyms, grammatical information, sociolinguistic information, and etymology. While writing the definition of the above 539 words, the list of the defining vocabulary was constructed, based on frequent vocabulary used in a Japanese monolingual dictionary. Although the concept of DCJAL has been almost perfected, it may need some more adjustment, and the research is continued. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=monolingual%20dictionary" title="monolingual dictionary">monolingual dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20Japanese%20language" title=" the Japanese language"> the Japanese language</a>, <a href="https://publications.waset.org/abstracts/search?q=non-native%20speaker%20of%20Japanese" title=" non-native speaker of Japanese"> non-native speaker of Japanese</a>, <a href="https://publications.waset.org/abstracts/search?q=defining%20vocabulary" title=" defining vocabulary"> defining vocabulary</a> </p> <a href="https://publications.waset.org/abstracts/186126/the-first-japanese-japanese-dictionary-for-non-japanese-using-the-defining-vocabulary" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186126.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">41</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">84</span> Effects of Computer-Mediated Dictionaries on Reading Comprehension and Vocabulary Acquisition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Amin%20Mekheimer">Mohamed Amin Mekheimer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aimed to investigate the effects of paper-based monolingual, pop-up and type-in electronic dictionaries on improving reading comprehension and incidental vocabulary acquisition and retention in an EFL context. It tapped into how computer-mediated dictionaries may have facilitated/impeded reading comprehension and vocabulary acquisition. Findings showed differential effects produced by the three treatments compared with the control group. Specifically, it revealed that the pop-up dictionary condition had the shortest average vocabulary searching time, vocabulary and text reading time, yet with less than the type-in dictionary group but more than the book dictionary group in terms of frequent dictionary 'look-ups' (p<.0001). In addition, ANOVA analyses also showed that text reading time differed significantly across all four treatments, and so did reading comprehension. Vocabulary acquisition was reported as enhanced in the three treatments rather than in the control group, but still with insignificant differences across the three treatments, yet with more differential effects in favour of the pop-up condition. Data also assert that participants preferred the pop-up e-dictionary more than the type-in and paper-based groups. Explanations of the findings vis-à-vis the cognitive load theory were presented. Pedagogical implications and suggestions for further research were forwarded at the end. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer-mediated%20dictionaries" title="computer-mediated dictionaries">computer-mediated dictionaries</a>, <a href="https://publications.waset.org/abstracts/search?q=type-in%20dictionaries" title=" type-in dictionaries"> type-in dictionaries</a>, <a href="https://publications.waset.org/abstracts/search?q=pop-up%20dictionaries" title=" pop-up dictionaries"> pop-up dictionaries</a>, <a href="https://publications.waset.org/abstracts/search?q=reading%20comprehension" title=" reading comprehension"> reading comprehension</a>, <a href="https://publications.waset.org/abstracts/search?q=vocabulary%20acquisition" title=" vocabulary acquisition"> vocabulary acquisition</a> </p> <a href="https://publications.waset.org/abstracts/15403/effects-of-computer-mediated-dictionaries-on-reading-comprehension-and-vocabulary-acquisition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15403.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">435</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">83</span> Sparse Coding Based Classification of Electrocardiography Signals Using Data-Driven Complete Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20Noman">Fuad Noman</a>, <a href="https://publications.waset.org/abstracts/search?q=Sh-Hussain%20Salleh"> Sh-Hussain Salleh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee-Ming%20Ting"> Chee-Ming Ting</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadri%20Hussain"> Hadri Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Rasul"> Syed Rasul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title=" dictionary learning"> dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20coding" title=" sparse coding"> sparse coding</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/52418/sparse-coding-based-classification-of-electrocardiography-signals-using-data-driven-complete-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52418.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">82</span> Project Marayum: Creating a Community Built Mobile Phone Based, Online Web Dictionary for Endangered Philippine Languages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samantha%20Jade%20Sadural">Samantha Jade Sadural</a>, <a href="https://publications.waset.org/abstracts/search?q=Kathleen%20Gay%20Figueroa"> Kathleen Gay Figueroa</a>, <a href="https://publications.waset.org/abstracts/search?q=Noel%20Nicanor%20Sison%20II"> Noel Nicanor Sison II</a>, <a href="https://publications.waset.org/abstracts/search?q=Francis%20Miguel%20Quilab"> Francis Miguel Quilab</a>, <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Edric%20Solis"> Samuel Edric Solis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kiel%20Gonzales"> Kiel Gonzales</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20Andrew%20Boquiren"> Alain Andrew Boquiren</a>, <a href="https://publications.waset.org/abstracts/search?q=Janelle%20Tan"> Janelle Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Carreon"> Mario Carreon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Of the 185 languages in the Philippines, 28 are endangered, 11 are dying off, and 4 are extinct. Language documentation, as a prerequisite to language education, can be one of the ways languages can be preserved. Project Marayum is envisioned to be a collaboratively built, mobile phone-based, online dictionary platform for Philippine languages. Although there are many online language dictionaries available on the Internet, Project Marayum aims to give a sense of ownership to the language community's dictionary as it is built and maintained by the community for the community. From a seed dictionary, members of a language community can suggest changes, add new entries, and provide language examples. Going beyond word definitions, the platform can be used to gather sample sentences and even audio samples of word usage. These changes are reviewed by language experts of the community, sourced from the local state universities or local government units. Approved changes are then added to the dictionary and can be viewed instantly through the Marayum website. A companion mobile phone application allows users to browse the dictionary in remote areas where Internet connectivity is nonexistent. The dictionary will automatically be updated once the user regains Internet access. Project Marayum is still a work in progress. At the time of this abstract's writing, the Project has just entered its second year. Prototypes are currently being tested with the Asi language of Romblon island as its initial language testbed. In October 2020, Project Marayum will have both a webpage and mobile application with Asi, Ilocano, and Cebuano language dictionaries available for use online or for download. In addition, the Marayum platform would be then easily expandable for use of the more endangered language communities. Project Marayum is funded by the Philippines Department of Science and Technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collaborative%20language%20dictionary" title="collaborative language dictionary">collaborative language dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=community-centered%20lexicography" title=" community-centered lexicography"> community-centered lexicography</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20management%20system" title=" content management system"> content management system</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20engineering" title=" software engineering"> software engineering</a> </p> <a href="https://publications.waset.org/abstracts/115655/project-marayum-creating-a-community-built-mobile-phone-based-online-web-dictionary-for-endangered-philippine-languages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115655.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">81</span> Online Multilingual Dictionary Using Hamburg Notation for Avatar-Based Indian Sign Language Generation System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sugandhi">Sugandhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Parteek%20Kumar"> Parteek Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanmeet%20Kaur"> Sanmeet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sign Language (SL) is used by deaf and other people who cannot speak but can hear or have a problem with spoken languages due to some disability. It is a visual gesture language that makes use of either one hand or both hands, arms, face, body to convey meanings and thoughts. SL automation system is an effective way which provides an interface to communicate with normal people using a computer. In this paper, an avatar based dictionary has been proposed for text to Indian Sign Language (ISL) generation system. This research work will also depict a literature review on SL corpus available for various SL s over the years. For ISL generation system, a written form of SL is required and there are certain techniques available for writing the SL. The system uses Hamburg sign language Notation System (HamNoSys) and Signing Gesture Mark-up Language (SiGML) for ISL generation. It is developed in PHP using Web Graphics Library (WebGL) technology for 3D avatar animation. A multilingual ISL dictionary is developed using HamNoSys for both English and Hindi Language. This dictionary will be used as a database to associate signs with words or phrases of a spoken language. It provides an interface for admin panel to manage the dictionary, i.e., modification, addition, or deletion of a word. Through this interface, HamNoSys can be developed and stored in a database and these notations can be converted into its corresponding SiGML file manually. The system takes natural language input sentence in English and Hindi language and generate 3D sign animation using an avatar. SL generation systems have potential applications in many domains such as healthcare sector, media, educational institutes, commercial sectors, transportation services etc. This research work will help the researchers to understand various techniques used for writing SL and generation of Sign Language systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=avatar" title="avatar">avatar</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary" title=" dictionary"> dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=HamNoSys" title=" HamNoSys"> HamNoSys</a>, <a href="https://publications.waset.org/abstracts/search?q=hearing%20impaired" title=" hearing impaired"> hearing impaired</a>, <a href="https://publications.waset.org/abstracts/search?q=Indian%20sign%20language%20%28ISL%29" title=" Indian sign language (ISL)"> Indian sign language (ISL)</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language" title=" sign language"> sign language</a> </p> <a href="https://publications.waset.org/abstracts/88342/online-multilingual-dictionary-using-hamburg-notation-for-avatar-based-indian-sign-language-generation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88342.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">230</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">80</span> Sparse Representation Based Spatiotemporal Fusion Employing Additional Image Pairs to Improve Dictionary Training</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dacheng%20Li">Dacheng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Huang"> Bo Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qinjin%20Han"> Qinjin Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Li"> Ming Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatiotemporal%20fusion" title="spatiotemporal fusion">spatiotemporal fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=K-SVD%20algorithm" title=" K-SVD algorithm"> K-SVD algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title=" dictionary learning"> dictionary learning</a> </p> <a href="https://publications.waset.org/abstracts/74785/sparse-representation-based-spatiotemporal-fusion-employing-additional-image-pairs-to-improve-dictionary-training" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74785.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">79</span> Design and Implementation of Image Super-Resolution for Myocardial Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Chidananda%20Murthy">M. V. Chidananda Murthy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Kurian"> M. Z. Kurian</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Guruprasad"> H. S. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Super-resolution is the technique of intelligently upscaling images, avoiding artifacts or blurring, and deals with the recovery of a high-resolution image from one or more low-resolution images. Single-image super-resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super-resolution has been demonstrated to improve image quality in scaled down images in the image domain, its effects on the Fourier-based technique remains unknown. Super-resolution substantially improved the spatial resolution of the patient LGE images by sharpening the edges of the heart and the scar. This paper aims at investigating the effects of single image super-resolution on Fourier-based and image based methods of scale-up. In this paper, first, generate a training phase of the low-resolution image and high-resolution image to obtain dictionary. In the test phase, first, generate a patch and then difference of high-resolution image and interpolation image from the low-resolution image. Next simulation of the image is obtained by applying convolution method to the dictionary creation image and patch extracted the image. Finally, super-resolution image is obtained by combining the fused image and difference of high-resolution and interpolated image. Super-resolution reduces image errors and improves the image quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20dictionary%20creation" title="image dictionary creation">image dictionary creation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20super-resolution" title=" image super-resolution"> image super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=LGE%20images" title=" LGE images"> LGE images</a>, <a href="https://publications.waset.org/abstracts/search?q=patch%20extraction" title=" patch extraction"> patch extraction</a> </p> <a href="https://publications.waset.org/abstracts/59494/design-and-implementation-of-image-super-resolution-for-myocardial-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59494.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">78</span> The Automatisation of Dictionary-Based Annotation in a Parallel Corpus of Old English</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20Elvira%20Ojanguren%20Lopez">Ana Elvira Ojanguren Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=Javier%20Martin%20Arista"> Javier Martin Arista</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aims of this paper are to present the automatisation procedure adopted in the implementation of a parallel corpus of Old English, as well as, to assess the progress of automatisation with respect to tagging, annotation, and lemmatisation. The corpus consists of an aligned parallel text with word-for-word comparison Old English-English that provides the Old English segment with inflectional form tagging (gloss, lemma, category, and inflection) and lemma annotation (spelling, meaning, inflectional class, paradigm, word-formation and secondary sources). This parallel corpus is intended to fill a gap in the field of Old English, in which no parallel and/or lemmatised corpora are available, while the average amount of corpus annotation is low. With this background, this presentation has two main parts. The first part, which focuses on tagging and annotation, selects the layouts and fields of lexical databases that are relevant for these tasks. Most information used for the annotation of the corpus can be retrieved from the lexical and morphological database Nerthus and the database of secondary sources Freya. These are the sources of linguistic and metalinguistic information that will be used for the annotation of the lemmas of the corpus, including morphological and semantic aspects as well as the references to the secondary sources that deal with the lemmas in question. Although substantially adapted and re-interpreted, the lemmatised part of these databases draws on the standard dictionaries of Old English, including The Student's Dictionary of Anglo-Saxon, An Anglo-Saxon Dictionary, and A Concise Anglo-Saxon Dictionary. The second part of this paper deals with lemmatisation. It presents the lemmatiser Norna, which has been implemented on Filemaker software. It is based on a concordance and an index to the Dictionary of Old English Corpus, which comprises around three thousand texts and three million words. In its present state, the lemmatiser Norna can assign lemma to around 80% of textual forms on an automatic basis, by searching the index and the concordance for prefixes, stems and inflectional endings. The conclusions of this presentation insist on the limits of the automatisation of dictionary-based annotation in a parallel corpus. While the tagging and annotation are largely automatic even at the present stage, the automatisation of alignment is pending for future research. Lemmatisation and morphological tagging are expected to be fully automatic in the near future, once the database of secondary sources Freya and the lemmatiser Norna have been completed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpus%20linguistics" title="corpus linguistics">corpus linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20linguistics" title=" historical linguistics"> historical linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=old%20English" title=" old English"> old English</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20corpus" title=" parallel corpus"> parallel corpus</a> </p> <a href="https://publications.waset.org/abstracts/88538/the-automatisation-of-dictionary-based-annotation-in-a-parallel-corpus-of-old-english" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">212</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">77</span> Sentiment Classification Using Enhanced Contextual Valence Shifters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vo%20Ngoc%20Phu">Vo Ngoc Phu</a>, <a href="https://publications.waset.org/abstracts/search?q=Phan%20Thi%20Tuoi"> Phan Thi Tuoi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20classification" title="sentiment classification">sentiment classification</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20orientation" title=" sentiment orientation"> sentiment orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=valence%20shifters" title=" valence shifters"> valence shifters</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual" title=" contextual"> contextual</a>, <a href="https://publications.waset.org/abstracts/search?q=valence%20shifters" title=" valence shifters"> valence shifters</a>, <a href="https://publications.waset.org/abstracts/search?q=term%20counting" title=" term counting"> term counting</a> </p> <a href="https://publications.waset.org/abstracts/11410/sentiment-classification-using-enhanced-contextual-valence-shifters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11410.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">503</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">76</span> Examining the Function of Containers and Determining Lexical Indices for the Shapes of Pottery and the Poems Written on Them from the End of the 3rd Century to the End of the 8th Century</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohadese%20Sookhtesaraii">Mohadese Sookhtesaraii</a>, <a href="https://publications.waset.org/abstracts/search?q=Abed%20Taghavi"> Abed Taghavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kosar%20Sookhtesaraii"> Kosar Sookhtesaraii</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pottery is always attended by human beings for its application functions. By passing time and human development and writing progressing, writing was started to do on pottery dishes. Some of important issues in making thise dishes, in addition to their application, are their names and obviosely their relationship between their function and their names. These names are different based on their appearances and the kind of their using. So by meaning these words in dictionary, naming these dishes are classified. In poetry works there are so many names of these dishes which are showing their importance and their using. More using of some of these dishes name in poem and writing works is caused the select these dishes. For better and precise analysing the form of pottery it emphasis on the meaning which are in dictionary and the names that are existed in poems and writters works. On the other hand, on the dishes there are written poet more than text, that it can study their beautiful aspect. Seperate from their meanings. Dishes name like Chamaneh, Satgini, was clearly named for drinking in dictionary. while using Khonb was applied for storing. So dishes applying can be the basis of classifying. The size and capacity of these dishes is also caused the differences in naming the dishes. Such as Khom, Khonb which are same in farm but. They are different in capacity and size. Meaning are written on these dishe was studied. In addition to preying phrase, they had loving meaning or inviting to drink and enjoying and shorting the human life. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pialeh" title="pialeh">pialeh</a>, <a href="https://publications.waset.org/abstracts/search?q=sajegni" title=" sajegni"> sajegni</a>, <a href="https://publications.waset.org/abstracts/search?q=khomre" title=" khomre"> khomre</a>, <a href="https://publications.waset.org/abstracts/search?q=pottery" title=" pottery"> pottery</a> </p> <a href="https://publications.waset.org/abstracts/174004/examining-the-function-of-containers-and-determining-lexical-indices-for-the-shapes-of-pottery-and-the-poems-written-on-them-from-the-end-of-the-3rd-century-to-the-end-of-the-8th-century" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174004.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">75</span> A Comparative Analysis of Vocabulary Learning Strategies among EFL Freshmen and Senior Medical Sciences Students across Different Fields of Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Hadavi">M. Hadavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Hashemi"> Z. Hashemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Learning strategies play an important role in the development of language skills. Vocabulary learning strategies as the backbone of these strategies have become a major part of English language teaching. This study is a comparative analysis of Vocabulary Learning Strategies (VLS) use and preference among freshmen and senior EFL medical sciences students with different fields of study. 449 students (236 freshman and 213 seniors) participated in the study. 64.6% were female and 35.4% were male. The instrument utilized in this research was a questionnaire consisting of 41 items related to the students’ approach to vocabulary learning. The items were classified under eight sections as dictionary strategies, guessing strategies, study preferences, memory strategies, autonomy, note- taking strategies, selective attention, and social strategies. The participants were asked to answer each item with a 5-point Likert-style frequency scale as follows:1) I never or almost never do this, 2) I don’t usually do this, 3) I sometimes do this, 4) I usually do this, and 5)I always or almost always do this. The results indicated that freshmen students and particularly surgical technology students used more strategies compared to the seniors. Overall guessing and dictionary strategies were the most frequently used strategies among all the learners (p=0/000). The mean and standard deviation of using VLS in the students who had no previous history of participating in the private English language classes was less than the students who had attended these type of classes (p=0/000). Female students tended to use social and study preference strategies whereas male students used mostly guessing and dictionary strategies. It can be concluded that the senior students under instruction from the university have learned to rely on themselves and choose the autonomous strategies more, while freshmen students use more strategies that are related to the study preferences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vocabulary%20leaning%20strategies" title="vocabulary leaning strategies">vocabulary leaning strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20sciences" title=" medical sciences"> medical sciences</a>, <a href="https://publications.waset.org/abstracts/search?q=students" title=" students"> students</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistics" title=" linguistics"> linguistics</a> </p> <a href="https://publications.waset.org/abstracts/10367/a-comparative-analysis-of-vocabulary-learning-strategies-among-efl-freshmen-and-senior-medical-sciences-students-across-different-fields-of-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">451</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">74</span> The French Ekang Ethnographic Dictionary. The Quantum Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Henda%20Gnakate%20Biba">Henda Gnakate Biba</a>, <a href="https://publications.waset.org/abstracts/search?q=Ndassa%20%20Mouafon%20Issa"> Ndassa Mouafon Issa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dictionaries modeled on the Western model [tonic accent languages] are not suitable and do not account for tonal languages phonologically, which is why the [prosodic and phonological] ethnographic dictionary was designed. It is a glossary that expresses the tones and the rhythm of words. It recreates exactly the speaking or singing of a tonal language, and allows the non-speaker of this language to pronounce the words as if they were a native. It is a dictionary adapted to tonal languages. It was built from ethnomusicological theorems and phonological processes, according to Jean. J. Rousseau 1776 hypothesis /To say and to sing were once the same thing/. Each word in the French dictionary finds its corresponding language, ekaη. And each word ekaη is written on a musical staff. This ethnographic dictionary is also an inventive, original and innovative research thesis, but it is also an inventive, original and innovative research thesis. A contribution to the theoretical, musicological, ethno musicological and linguistic conceptualization of languages, giving rise to the practice of interlocution between the social and cognitive sciences, the activities of artistic creation and the question of modeling in the human sciences: mathematics, computer science, translation automation and artificial intelligence. When you apply this theory to any text of a folksong of a world-tone language, you do not only piece together the exact melody, rhythm, and harmonies of that song as if you knew it in advance but also the exact speaking of this language. The author believes that the issue of the disappearance of tonal languages and their preservation has been structurally resolved, as well as one of the greatest cultural equations related to the composition and creation of tonal, polytonal and random music. The experimentation confirming the theorization designed a semi-digital, semi-analog application which translates the tonal languages of Africa (about 2,100 languages) into blues, jazz, world music, polyphonic music, tonal and anatonal music and deterministic and random music). To test this application, I use a music reading and writing software that allows me to collect the data extracted from my mother tongue, which is already modeled in the musical staves saved in the ethnographic (semiotic) dictionary for automatic translation ( volume 2 of the book). Translation is done (from writing to writing, from writing to speech and from writing to music). Mode of operation: you type a text on your computer, a structured song (chorus-verse), and you command the machine a melody of blues, jazz and, world music or, variety etc. The software runs, giving you the option to choose harmonies, and then you select your melody. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=music" title="music">music</a>, <a href="https://publications.waset.org/abstracts/search?q=language" title=" language"> language</a>, <a href="https://publications.waset.org/abstracts/search?q=entenglement" title=" entenglement"> entenglement</a>, <a href="https://publications.waset.org/abstracts/search?q=science" title=" science"> science</a>, <a href="https://publications.waset.org/abstracts/search?q=research" title=" research"> research</a> </p> <a href="https://publications.waset.org/abstracts/174989/the-french-ekang-ethnographic-dictionary-the-quantum-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174989.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">73</span> Atwood's Canadianisms and Neologisms: A Cognitive Approach to Literature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eleonora%20Sasso">Eleonora Sasso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper takes as its starting point the notions of cognitive linguistics and lexical blending, and uses both these theoretical concepts to advance a new reading of Margaret Atwood’s latest writings, one which sees them as paramount literary examples of norm and usage in bilingual Canadian lexicography. Atwood’s prose seems to be imbued with Canadianisms and neologisms, lexical blends of zoomorphic forms, a kind of meeting-point between two conceptual structures which follow the principles of lexical economy and asyntactic relation. Atwood’s neologisms also attest to the undeniable impact on language exerted by Canada’s aboriginal peoples. This paper aims to track through these references and with the aid of the Eskimo-English dictionary look at the linguistic issues – attitudes to contaminations and hybridisations, questions of lexical blending in literary examples, etc – which they raise. Atwood’s fiction, whose cognitive linguistic strategy employs ‘the virtues of scissors and matches’, always strives to achieve isomorphism between word form and concept. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atwood" title="Atwood">Atwood</a>, <a href="https://publications.waset.org/abstracts/search?q=Canadianisms" title=" Canadianisms"> Canadianisms</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20science" title=" cognitive science"> cognitive science</a>, <a href="https://publications.waset.org/abstracts/search?q=Eskimo%2FEnglish%20dictionary" title=" Eskimo/English dictionary"> Eskimo/English dictionary</a> </p> <a href="https://publications.waset.org/abstracts/70217/atwoods-canadianisms-and-neologisms-a-cognitive-approach-to-literature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70217.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">264</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">72</span> Experimental Evaluation of Succinct Ternary Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dmitriy%20Kuptsov">Dmitriy Kuptsov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tree data structures, such as binary or in general k-ary trees, are essential in computer science. The applications of these data structures can range from data search and retrieval to sorting and ranking algorithms. Naive implementations of these data structures can consume prohibitively large volumes of random access memory limiting their applicability in certain solutions. Thus, in these cases, more advanced representation of these data structures is essential. In this paper we present the design of the compact version of ternary tree data structure and demonstrate the results for the experimental evaluation using static dictionary problem. We compare these results with the results for binary and regular ternary trees. The conducted evaluation study shows that our design, in the best case, consumes up to 12 times less memory (for the dictionary used in our experimental evaluation) than a regular ternary tree and in certain configuration shows performance comparable to regular ternary trees. We have evaluated the performance of the algorithms using both 32 and 64 bit operating systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20structures" title="data structures">data structures</a>, <a href="https://publications.waset.org/abstracts/search?q=succinct%20ternary%20tree" title="succinct ternary tree">succinct ternary tree</a>, <a href="https://publications.waset.org/abstracts/search?q=per-%20formance%20evaluation" title="per- formance evaluation">per- formance evaluation</a> </p> <a href="https://publications.waset.org/abstracts/144336/experimental-evaluation-of-succinct-ternary-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144336.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">160</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">71</span> A Motion Dictionary to Real-Time Recognition of Sign Language Alphabet Using Dynamic Time Warping and Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marcio%20Leal">Marcio Leal</a>, <a href="https://publications.waset.org/abstracts/search?q=Marta%20Villamil"> Marta Villamil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computacional recognition of sign languages aims to allow a greater social and digital inclusion of deaf people through interpretation of their language by computer. This article presents a model of recognition of two of global parameters from sign languages; hand configurations and hand movements. Hand motion is captured through an infrared technology and its joints are built into a virtual three-dimensional space. A Multilayer Perceptron Neural Network (MLP) was used to classify hand configurations and Dynamic Time Warping (DWT) recognizes hand motion. Beyond of the method of sign recognition, we provide a dataset of hand configurations and motion capture built with help of fluent professionals in sign languages. Despite this technology can be used to translate any sign from any signs dictionary, Brazilian Sign Language (Libras) was used as case study. Finally, the model presented in this paper achieved a recognition rate of 80.4%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20time%20warping" title=" dynamic time warping"> dynamic time warping</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared" title=" infrared"> infrared</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language%20recognition" title=" sign language recognition"> sign language recognition</a> </p> <a href="https://publications.waset.org/abstracts/94322/a-motion-dictionary-to-real-time-recognition-of-sign-language-alphabet-using-dynamic-time-warping-and-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94322.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">216</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">70</span> KSVD-SVM Approach for Spontaneous Facial Expression Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dawood%20Al%20Chanti">Dawood Al Chanti</a>, <a href="https://publications.waset.org/abstracts/search?q=Alice%20Caplier"> Alice Caplier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title="dictionary learning">dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20projection" title=" random projection"> random projection</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20and%20spontaneous%20facial%20expression" title=" pose and spontaneous facial expression"> pose and spontaneous facial expression</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/51683/ksvd-svm-approach-for-spontaneous-facial-expression-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">69</span> Bag of Local Features for Person Re-Identification on Large-Scale Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yixiu%20Liu">Yixiu Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunzhou%20Zhang"> Yunzhou Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianning%20Chi"> Jianning Chi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Chu"> Hao Chu</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui%20Zheng"> Rui Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Libo%20Sun"> Libo Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Guanghao%20Chen"> Guanghao Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Fangtong%20Zhou"> Fangtong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last few years, large-scale person re-identification has attracted a lot of attention from video surveillance since it has a potential application prospect in public safety management. However, it is still a challenging job considering the variation in human pose, the changing illumination conditions and the lack of paired samples. Although the accuracy has been significantly improved, the data dependence of the sample training is serious. To tackle this problem, a new strategy is proposed based on bag of visual words (BoVW) model of designing the feature representation which has been widely used in the field of image retrieval. The local features are extracted, and more discriminative feature representation is obtained by cross-view dictionary learning (CDL), then the assignment map is obtained through k-means clustering. Finally, the BoVW histograms are formed which encodes the images with the statistics of the feature classes in the assignment map. Experiments conducted on the CUHK03, Market1501 and MARS datasets show that the proposed method performs favorably against existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20visual%20words" title="bag of visual words">bag of visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-view%20dictionary%20learning" title=" cross-view dictionary learning"> cross-view dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20re-identification" title=" person re-identification"> person re-identification</a>, <a href="https://publications.waset.org/abstracts/search?q=reranking" title=" reranking"> reranking</a> </p> <a href="https://publications.waset.org/abstracts/85908/bag-of-local-features-for-person-re-identification-on-large-scale-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">68</span> A Method for the Extraction of the Character's Tendency from Korean Novels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min-Ha%20Hong">Min-Ha Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kee-Won%20Kim"> Kee-Won Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung-Hoon%20Kim"> Seung-Hoon Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The character in the story-based content, such as novels and movies, is one of the core elements to understand the story. In particular, the character’s tendency is an important factor to analyze the story-based content, because it has a significant influence on the storyline. If readers have the knowledge of the tendency of characters before reading a novel, it will be helpful to understand the structure of conflict, episode and relationship between characters in the novel. It may therefore help readers to select novel that the reader wants to read. In this paper, we propose a method of extracting the tendency of the characters from a novel written in Korean. In advance, we build the dictionary with pairs of the emotional words in Korean and English since the emotion words in the novel’s sentences express character’s feelings. We rate the degree of polarity (positive or negative) of words in our emotional words dictionary based on SenticNet. Then we extract characters and emotion words from sentences in a novel. Since the polarity of a word grows strong or weak due to sentence features such as quotations and modifiers, our proposed method consider them to calculate the polarity of characters. The information of the extracted character’s polarity can be used in the book search service or book recommendation service. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20tendency" title="character tendency">character tendency</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20word" title=" emotion word"> emotion word</a>, <a href="https://publications.waset.org/abstracts/search?q=Korean%20novel" title=" Korean novel"> Korean novel</a> </p> <a href="https://publications.waset.org/abstracts/47141/a-method-for-the-extraction-of-the-characters-tendency-from-korean-novels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47141.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">67</span> A Recognition Method for Spatio-Temporal Background in Korean Historical Novels </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seo-Hee%20Kim">Seo-Hee Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kee-Won%20Kim"> Kee-Won Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung-Hoon%20Kim"> Seung-Hoon Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most important elements of a novel are the characters, events and background. The background represents the time, place and situation that character appears, and conveys event and atmosphere more realistically. If readers have the proper knowledge about background of novels, it may be helpful for understanding the atmosphere of a novel and choosing a novel that readers want to read. In this paper, we are targeting Korean historical novels because spatio-temporal background especially performs an important role in historical novels among the genre of Korean novels. To the best of our knowledge, we could not find previous study that was aimed at Korean novels. In this paper, we build a Korean historical national dictionary. Our dictionary has historical places and temple names of kings over many generations as well as currently existing spatial words or temporal words in Korean history. We also present a method for recognizing spatio-temporal background based on patterns of phrasal words in Korean sentences. Our rules utilize postposition for spatial background recognition and temple names for temporal background recognition. The knowledge of the recognized background can help readers to understand the flow of events and atmosphere, and can use to visualize the elements of novels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Korean%20historical%20novels" title=" Korean historical novels"> Korean historical novels</a>, <a href="https://publications.waset.org/abstracts/search?q=Korean%20linguistic%20feature" title=" Korean linguistic feature"> Korean linguistic feature</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20background" title=" spatio-temporal background"> spatio-temporal background</a> </p> <a href="https://publications.waset.org/abstracts/47144/a-recognition-method-for-spatio-temporal-background-in-korean-historical-novels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">66</span> Curriculum Check in Industrial Design, Based on Knowledge Management in Iran Universities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Mostafaee">Maryam Mostafaee</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Sadeghi%20Naeini"> Hassan Sadeghi Naeini</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Mostowfi"> Sara Mostowfi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today’s Knowledge management (KM), plays an important role in organizations. Basically, knowledge management is in the relation of using it for taking advantage of work forces in an organization for forwarding the goals and demand of that organization used at the most. The purpose of knowledge management is not only to manage existing documentation, information, and Data through an organization, but the most important part of KM is to control most important and key factor of those information and Data. For sure it is to chase the information needed for the employees in the right time of needed to take from genuine source for bringing out the best performance and result then in this matter the performance of organization will be at most of it. There are a lot of definitions over the objective of management released. Management is the science that in force the accurate knowledge with repeating to the organization to shape it and take full advantages for reaching goals and targets in the organization to be used by employees and users, but the definition of Knowledge based on Kalinz dictionary is: Facts, emotions or experiences known by man or group of people is ‘ knowledge ‘: Based on the Merriam Webster Dictionary: the act or skill of controlling and making decision about a business, department, sport team, etc, based on the Oxford Dictionary: Efficient handling of information and resources within a commercial organization, and based on the Oxford Dictionary: The art or process of designing manufactured products: the scale is a beautiful work of industrial design. When knowledge management performed executive in universities, discovery and create a new knowledge be facilitated. Make procedures between different units for knowledge exchange. College's officials and employees understand the importance of knowledge for University's success and will make more efforts to prevent the errors. In this strategy, is explored factors and affective trends and manage of it in University. In this research, Iranian universities for a time being analyzed that over usage of knowledge management, how they are behaving and having understood this matter: 1. Discovery of knowledge management in Iranian Universities, 2. Transferring exciting knowledge between faculties and unites, 3. Participate of employees for getting and using and transferring knowledge, 4.The accessibility of valid sources, 5. Researching over factors and correct processes in the university. We are pointing in some examples that we have already analyzed which is: -Enabling better and faster decision-making, -Making it easy to find relevant information and resources, -Reusing ideas, documents, and expertise, -Avoiding redundant effort. Consequence: It is found that effectiveness of knowledge management in the Industrial design field is low. Based on filled checklist by Education officials and professors in universities, and coefficient of effectiveness Calculate, knowledge management could not get the right place. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title="knowledge management">knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20design" title=" industrial design"> industrial design</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20curriculum" title=" educational curriculum"> educational curriculum</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20performance" title=" learning performance"> learning performance</a> </p> <a href="https://publications.waset.org/abstracts/33880/curriculum-check-in-industrial-design-based-on-knowledge-management-in-iran-universities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33880.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">65</span> Technique and Use of Machine Readable Dictionary: In Special Reference to Hindi-Marathi Machine Translation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milind%20Patil">Milind Patil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Present paper is a discussion on Hindi-Marathi Morphological Analysis and generating rules for Machine Translation on the basis of Machine Readable Dictionary (MRD). This used Transformative Generative Grammar (TGG) rules to design the MRD. As per TGG rules, the suffix of a particular root word is based on its Tense, Aspect, Modality and Voice. That's why the suffix is very important for the word meanings (or root meanings). The Hindi and Marathi Language both have relation with Indo-Aryan language family. Both have been derived from Sanskrit language and their script is 'Devnagari'. But there are lots of differences in terms of semantics and grammatical level too. In Marathi, there are three genders, but in Hindi only two (Masculine and Feminine), the Natural gender is absent in Hindi. Likewise other grammatical categories also differ in their level of use. For MRD the suffixes (or Morpheme) are of particular root word for GNP (Gender, Number and Person) are based on its natural phenomena. A particular Suffix and Morphine change as per the need of person, number and gender. The design of MRD also based on this format. In first, Person, Number, Gender and Tense are key points than root words and suffix of particular Person, Number Gender (PNG). After that the inferences are drawn on the basis of rules that is (V.stem) (Pre.T/Past.T) (x) + (Aux-Pre.T) (x) → (V.Stem.) + (SP.TM) (X). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRD" title="MRD">MRD</a>, <a href="https://publications.waset.org/abstracts/search?q=TGG" title=" TGG"> TGG</a>, <a href="https://publications.waset.org/abstracts/search?q=stem" title=" stem"> stem</a>, <a href="https://publications.waset.org/abstracts/search?q=morph" title=" morph"> morph</a>, <a href="https://publications.waset.org/abstracts/search?q=morpheme" title=" morpheme"> morpheme</a>, <a href="https://publications.waset.org/abstracts/search?q=suffix" title=" suffix"> suffix</a>, <a href="https://publications.waset.org/abstracts/search?q=PNG" title=" PNG"> PNG</a>, <a href="https://publications.waset.org/abstracts/search?q=TAM%26V" title=" TAM&V"> TAM&V</a>, <a href="https://publications.waset.org/abstracts/search?q=root" title=" root"> root</a> </p> <a href="https://publications.waset.org/abstracts/69852/technique-and-use-of-machine-readable-dictionary-in-special-reference-to-hindi-marathi-machine-translation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69852.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">64</span> Speaker Identification by Atomic Decomposition of Learned Features Using Computational Auditory Scene Analysis Principals in Noisy Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Bryan">Thomas Bryan</a>, <a href="https://publications.waset.org/abstracts/search?q=Veton%20Kepuska"> Veton Kepuska</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivica%20Kostanic"> Ivica Kostanic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time-frequency%20plane" title="time-frequency plane">time-frequency plane</a>, <a href="https://publications.waset.org/abstracts/search?q=atomic%20decomposition" title=" atomic decomposition"> atomic decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=envelope%20sampling" title=" envelope sampling"> envelope sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20atoms" title=" Gabor atoms"> Gabor atoms</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20pursuit" title=" matching pursuit"> matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20dictionary%20learning" title=" sparse dictionary learning"> sparse dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20autoencoder" title=" sparse autoencoder"> sparse autoencoder</a> </p> <a href="https://publications.waset.org/abstracts/51069/speaker-identification-by-atomic-decomposition-of-learned-features-using-computational-auditory-scene-analysis-principals-in-noisy-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">289</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">63</span> The Difference of Learning Outcomes in Reading Comprehension between Text and Film as The Media in Indonesian Language for Foreign Speaker in Intermediate Level</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siti%20Ayu%20Ningsih">Siti Ayu Ningsih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to find the differences outcomes in learning reading comprehension with text and film as media on Indonesian Language for foreign speaker (BIPA) learning at intermediate level. By using quantitative and qualitative research methods, the respondent of this study is a single respondent from D'Royal Morocco Integrative Islamic School in grade nine from secondary level. Quantitative method used to calculate the learning outcomes that have been given the appropriate action cycle, whereas qualitative method used to translate the findings derived from quantitative methods to be described. The technique used in this study is the observation techniques and testing work. Based on the research, it is known that the use of the text media is more effective than the film for intermediate level of Indonesian Language for foreign speaker learner. This is because, when using film the learner does not have enough time to take note the difficult vocabulary and don't have enough time to look for the meaning of the vocabulary from the dictionary. While the use of media texts shows the better effectiveness because it does not require additional time to take note the difficult words. For the words that are difficult or strange, the learner can immediately find its meaning from the dictionary. The presence of the text is also very helpful for Indonesian Language for foreign speaker learner to find the answers according to the questions more easily. By matching the vocabulary of the question into the text references. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Indonesian%20language%20for%20foreign%20speaker" title="Indonesian language for foreign speaker">Indonesian language for foreign speaker</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20outcome" title=" learning outcome"> learning outcome</a>, <a href="https://publications.waset.org/abstracts/search?q=media" title=" media"> media</a>, <a href="https://publications.waset.org/abstracts/search?q=reading%20comprehension" title=" reading comprehension"> reading comprehension</a> </p> <a href="https://publications.waset.org/abstracts/82676/the-difference-of-learning-outcomes-in-reading-comprehension-between-text-and-film-as-the-media-in-indonesian-language-for-foreign-speaker-in-intermediate-level" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82676.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">197</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">62</span> Computerized Analysis of Phonological Structure of 10,400 Brazilian Sign Language Signs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanessa%20G.%20Oliveira">Wanessa G. Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20C.%20Capovilla"> Fernando C. Capovilla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Capovilla and Raphael’s Libras Dictionary documents a corpus of 4,200 Brazilian Sign Language (Libras) signs. Duduchi and Capovilla’s software SignTracking permits users to retrieve signs even when ignoring the gloss corresponding to it and to discover the meaning of all 4,200 signs sign simply by clicking on graphic menus of the sign characteristics (phonemes). Duduchi and Capovilla have discovered that the ease with which any given sign can be retrieved is an inverse function of the average popularity of its component phonemes. Thus, signs composed of rare (distinct) phonemes are easier to retrieve than are those composed of common phonemes. SignTracking offers a means of computing the average popularity of the phonemes that make up each one of 4,200 signs. It provides a precise measure of the degree of ease with which signs can be retrieved, and sign meanings can be discovered. Duduchi and Capovilla’s logarithmic model proved valid: The degree with which any given sign can be retrieved is an inverse function of the arithmetic mean of the logarithm of the popularity of each component phoneme. Capovilla, Raphael and Mauricio’s New Libras Dictionary documents a corpus of 10,400 Libras signs. The present analysis revealed Libras DNA structure by mapping the incidence of 501 sign phonemes resulting from the layered distribution of five parameters: 163 handshape phonemes (CherEmes-ManusIculi); 34 finger shape phonemes (DactilEmes-DigitumIculi); 55 hand placement phonemes (ArtrotoToposEmes-ArticulatiLocusIculi); 173 movement dimension phonemes (CinesEmes-MotusIculi) pertaining to direction, frequency, and type; and 76 Facial Expression phonemes (MascarEmes-PersonalIculi). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brazilian%20sign%20language" title="Brazilian sign language">Brazilian sign language</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical%20retrieval" title=" lexical retrieval"> lexical retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=libras%20sign" title=" libras sign"> libras sign</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20phonology" title=" sign phonology"> sign phonology</a> </p> <a href="https://publications.waset.org/abstracts/86262/computerized-analysis-of-phonological-structure-of-10400-brazilian-sign-language-signs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86262.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dictionary%20iearning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dictionary%20iearning&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dictionary%20iearning&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dictionary%20iearning&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>